A signal pilot design and acquisition method for high frequency offset scenarios

Abstract

This invention provides a signal pilot design and acquisition method for scenarios with large frequency offset. It utilizes pre- and post-autocorrelation processing of the received signal at the receiver to obtain the signal acquisition and detection peak. During the autocorrelation calculation, the frequency offset error term of the received signal is canceled out, leaving only a fixed phase offset term caused by the frequency offset and the time interval of repeated pilots. This fixed phase offset only affects the sign polarity of the autocorrelation result, not the autocorrelation amplitude itself. In principle, this eliminates the influence of frequency offset error on signal acquisition. This invention completely eliminates the influence of frequency offset error in the received signal, greatly improving signal acquisition performance under low signal-to-noise ratio conditions, and enhancing the stability and communication quality of the communication system. Without increasing computational complexity, it effectively avoids interference from frequency offset error, achieving accurate signal acquisition.

Description

Technical Field

[0001] This invention relates to the field of signal processing, and in particular to a signal acquisition method. Background Technology

[0002] For communication scenarios with large frequency deviations, conventional pilot design and signal acquisition methods suffer from high miss rates and poor acquisition performance, failing to effectively achieve signal acquisition and synchronization. To address the signal acquisition problem in communication scenarios with large frequency deviations, this invention proposes a novel pilot design and signal acquisition algorithm.

[0003] Signal pilots are designed for use by receivers in signal acquisition and carrier recovery. Typically, a signal pilot is a sequence of signals known to both the transmitter and receiver. The receiver uses different acquisition methods to detect whether it has received the pilot sequence, thereby determining whether a target signal has arrived at the receiver.

[0004] In existing communication systems, receivers often employ a method of cross-correlation between local pilot sequences and received pilot sequences for signal acquisition. To combat large frequency offset errors, pilot design often utilizes the superposition of multiple short pilot cross-correlation sequences. The specific processing is as follows: First, real-time conjugate correlation processing is performed between the local pilot sequence and the received signal; second, the correlation values ​​within a single pilot sequence are summed, and the absolute values ​​of the correlation values ​​from multiple pilots are summed; third, the sum of the multiple pilot sequences from step two is compared with a set signal acquisition threshold. If the sum is higher than the threshold, the signal is considered to have been acquired.

[0005] In addition, time-domain energy detection (TDOD) is often used for signal acquisition in some high signal-to-noise ratio (SNR) communication systems. In TDOD, the receiver first sets an energy detection window of a specific length. Then, it sums the absolute values ​​of the received signal amplitudes within the detection window to obtain the detected energy. Finally, it determines whether the signal has arrived by checking if the energy difference between the two detection windows reaches a set threshold.

[0006] The current communication system uses an algorithm based on cross-correlation and accumulation of local pilot sequences and received signals. Since the local pilot sequence does not contain frequency offset information from the received signal, the frequency offset error always exists and cannot be canceled when the local pilot sequence is cross-correlated with the received sequence. This causes the correlation values ​​of individual pilot sequences to cancel each other out due to the difference in instantaneous phase offset when they are superimposed, resulting in missed signal acquisition. Furthermore, the problem of missed signal acquisition does not improve with the improvement of signal quality.

[0007] While signal acquisition algorithms based on time-domain energy detection are insensitive to frequency offset errors, they have high requirements for signal quality and can only be applied to high signal-to-noise ratio (SNR) communication scenarios. When signal quality is low, time-domain energy detection algorithms cannot effectively distinguish between valid signals and noise, and cannot meet the signal acquisition requirements of low SNR communication applications. Therefore, energy detection acquisition algorithms are rarely used in modern communication systems. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a signal pilot design and acquisition method for scenarios with large frequency offset, which can accurately detect and acquire signals with large frequency offset errors even at low signal-to-noise ratios. Compared with classic pilot sequence cross-correlation signal detection algorithms, the proposed algorithm can effectively avoid interference from frequency offset errors and achieve accurate signal acquisition without increasing computational complexity.

[0009] The specific steps of the technical solution adopted by the present invention to solve its technical problem are as follows:

[0010] Step 1: Let x be the baseband signal generated by the transmitter. When there is a carrier frequency offset f... e When the carrier phase shifts by φ, the received signal is... Where w is Gaussian white noise and t represents the sampling time;

[0011] The transmitting pilot sequence satisfies the requirement that the signal sequence contains two identical pilot signals P. m ={p1,p2,...,p m Two identical pilot signals are inserted consecutively or intermittently to fill in other data;

[0012] Step 2: At the receiving end, two signal sliding detection windows are set. The time length of a single sliding detection window is Δt, which is the same as the length of the pilot signal. The signal within the sliding detection window moves forward one sampling point at a time. Let the current received signal be r(t). Each time the sampled signal within the sliding detection window moves forward, the signal r within sliding detection window 1 is... w1 (t) and the signal r within the sliding detection window 2 w2 (t) performs conjugate correlation operations;

[0013] After obtaining c(t), the conjugate multiplication sequences within the sliding detection window are accumulated, and the absolute value of the superposition result is obtained:

[0014]

[0015] Among them, c r That is, the acquisition correlation peak value obtained by the receiver using the pilot sequence processing, and t0 is the start time of the sliding correlation calculation;

[0016] Step 3: Obtain the capture correlation peak c of the pilot sequence r Then, c r With the set capture threshold T cap Compare;

[0017] Step 4: If c r >T cap If c, it means a valid signal has been captured; if c r ≤T cap If no valid signal is found, return to step 2 to continue the signal search and capture process.

[0018] In step 1, taking continuous pilot insertion as an example, the transmitted signal after pilot insertion is:

[0019] tx={{P m},{P m},{D n}}, where D n ={d1,d2,...,d n The data sequence is a baseband user data sequence. The two pilot signals received by the receiver are as follows:

[0020]

[0021] Where, r p1 and r p2 These are the received pilot signal 1 and pilot signal 2, respectively; Δt is the time interval between the two pilot signals, which is also the time length of a single pilot sequence in the repeated pilot continuous insertion mode; w1 and w2 are the Gaussian white noise contained in the two pilot signals, respectively.

[0022] In step 2, the specific steps of the conjugate correlation operation are as follows:

[0023] First, calculate the conjugate multiplication sequence c(t) of the signals within the window:

[0024]

[0025] in, Indicates r w2 The conjugate of (t); when both repeated pilot sequences are completely within the sliding detection window, pilot signal 1 enters sliding detection window 1, and pilot signal 2 enters sliding detection window 2, i.e., r w1 (t)=r p1 And r w2 (t)=r p2 The conjugate multiplication of the two sliding detection window signals is calculated as follows:

[0026]

[0027] in, This represents the Gaussian white noise term, which is ignored in the calculation because Δt is a fixed value. Consider it as a fixed phase offset caused by the accumulation of frequency offset over time. The fixed phase offset has the same effect on all sampling points in the signal sequence and will not cause mutual cancellation when the signal correlation is accumulated. It can be seen that after the conjugate multiplication process, the frequency offset error term in the original received signal is eliminated. Therefore, the magnitude of the subsequent signal acquisition correlation peak is no longer affected by the frequency offset and is only related to the signal-to-noise ratio of the signal.

[0028] An electronic device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to perform the methods described above.

[0029] A computer-readable storage medium storing program code that can be invoked by a processor to perform the method described above.

[0030] The beneficial effect of this invention is to address the difficulty of signal detection and acquisition in existing communication systems under low signal-to-noise ratio and large frequency offset error communication application scenarios. This invention proposes a pilot autocorrelation signal acquisition algorithm based on repetitive pilot design. This algorithm utilizes the receiver to perform pre- and post-autocorrelation processing on the received signal to obtain the signal acquisition and detection peak value. During the autocorrelation calculation, the frequency offset error term of the received signal can be canceled out, leaving only the fixed phase offset term caused by the frequency offset and the time interval of the repetitive pilot. The fixed phase offset only affects the sign polarity of the autocorrelation processing result, and has no effect on the autocorrelation amplitude itself. Furthermore, the autocorrelation calculation result is ultimately processed by taking the absolute value; therefore, the influence of the fixed phase offset on the sign polarity can also be ignored, thus eliminating the influence of frequency offset error on signal acquisition in principle. Therefore, the autocorrelation signal acquisition algorithm based on repetitive pilot proposed in this invention can completely eliminate the influence of frequency offset error of the received signal, greatly improving the signal acquisition performance under low signal-to-noise ratio conditions, and improving the stability and communication quality of the communication system. Attached Figure Description

[0031] Figure 1 This invention presents a repetitive pilot design scheme for the transmitting end.

[0032] Figure 2 This is a flowchart of the calculation process of the present invention.

[0033] Figure 3 This paper compares the acquisition performance of the present invention with that of the traditional pilot cross-correlation acquisition algorithm at a frequency offset of 3 kHz.

[0034] Figure 4The signal-to-noise ratio is -1dB, representing the acquisition probability of the proposed scheme at different frequency offsets.

[0035] Figure 5 The acquisition probability of the traditional cross-correlation pilot acquisition scheme at different frequency offsets is given by a signal-to-noise ratio of -1dB. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] Step 1: Let x be the baseband signal generated by the transmitter. When there is a carrier frequency offset f... e When the carrier phase shifts by φ, the received signal is... Where w is Gaussian white noise and t represents the sampling time;

[0038] The transmitting pilot sequence satisfies the requirement that the signal sequence contains two identical pilot signals P. m ={p1,p2,...,p m Two identical pilot signals are inserted consecutively, or other data is added intermittently; taking continuous pilot insertion as an example, the transmitted signal after inserting the pilot is tx = {{P} m},{P m},{D n}}, where D n ={d1,d2,...,d n The data sequence is a baseband user data sequence. The two pilot signals received by the receiver are as follows:

[0039]

[0040] Where, r p1 and r p2 These are the received pilot signal 1 and pilot signal 2, respectively; Δt is the time interval between the two pilot signals, which is also the time length of a single pilot sequence in the repeated pilot continuous insertion mode; w1 and w2 are the Gaussian white noise contained in the two pilot signals, respectively;

[0041] Step 2: At the receiving end, two signal sliding detection windows are set. The time length of a single sliding detection window is Δt, which is the same as the length of the pilot signal. The signal within the sliding detection window moves forward one sampling point at a time. Let the current received signal be r(t). Each time the sampled signal within the sliding detection window moves forward, the signal r within sliding detection window 1 is... w1 (t) and the signal r within the sliding detection window 2 w2 (t) Perform conjugate correlation operations; first calculate the conjugate multiplication sequence c(t) of the signals within the window:

[0042]

[0043] in, Indicates r w2 The conjugate of (t); when both repeated pilot sequences are completely within the sliding detection window, pilot signal 1 enters sliding detection window 1, and pilot signal 2 enters sliding detection window 2, i.e., r w1 (t)=r p1 And r w2 (t)=r p2 The conjugate multiplication of the two sliding detection window signals is calculated as follows:

[0044]

[0045] in, This represents the Gaussian white noise term, which is ignored in the calculation because Δt is a fixed value. This can be viewed as a fixed phase offset resulting from the accumulation of frequency offset over time. Since the fixed phase offset has a consistent effect on all sampling points in the signal sequence, it does not cause mutual cancellation during signal correlation accumulation. It can be seen that after the above conjugate multiplication process, the frequency offset error term in the original received signal is eliminated. Therefore, the magnitude of the subsequent signal acquisition correlation peak is no longer affected by the frequency offset, but only by the signal-to-noise ratio.

[0046] After obtaining c(t), the conjugate multiplication sequences within the sliding detection window are accumulated, and the absolute value of the superposition result is obtained:

[0047]

[0048] Among them, c r That is, the acquisition correlation peak value obtained by the receiver using the pilot sequence processing, and t0 is the start time of the sliding correlation calculation;

[0049] Step 3: Obtain the capture correlation peak c of the pilot sequence r Then, c r With the set capture threshold T cap Compare;

[0050] Step 4: If c r >T cap If c, it means a valid signal has been captured; if c r ≤T cap If no valid signal is found, return to step 2 to continue the signal search and capture process.

[0051] To further illustrate the superiority of this invention in signal acquisition in a large frequency offset error communication system, a comparative simulation was conducted between the traditional algorithm and the algorithm proposed in this invention. The parameters set in the simulation are as follows: the length of a single pilot sequence is 256 modulation symbols, the number of pilot sequences is 2 segments, and the symbol rate is 16 Msps.

[0052] The simulation first compared the acquisition performance of the proposed signal acquisition algorithm based on repetitive pilot autocorrelation and the classic local pilot cross-correlation algorithm under different signal-to-noise ratios, with a frequency offset error of 3 kHz. The simulation results are as follows: Figure 3 As shown, the algorithm proposed in this invention can achieve a 100% acquisition probability when the signal-to-noise ratio is greater than or equal to -1dB. In contrast, the traditional local pilot cross-correlation algorithm is affected by frequency offset, causing the signals to cancel each other out when they are correlated, resulting in no obvious correlation peak. Even if the signal-to-noise ratio is increased to 5dB, the signal acquisition probability does not exceed 1%.

[0053] Subsequently, simulations were conducted to compare the acquisition performance of the proposed signal acquisition algorithm based on repetitive pilot autocorrelation with that of the classic local pilot cross-correlation algorithm under different frequency offset errors at a low signal-to-noise ratio of -1dB. The simulation results are as follows: Figure 4 , 5 As shown. Figure 4 It can be seen that the repetitive pilot autocorrelation signal acquisition algorithm proposed in this invention is insensitive to frequency offset error, and can maintain a 100% acquisition probability within a test range of frequency offset error from 0kHz to 120kHz. Figure 5 It can be seen that the acquisition performance of the traditional local pilot cross-correlation acquisition algorithm is significantly affected by the frequency offset. It can still guarantee 100% signal acquisition performance within the frequency offset error range of 0KHz to 2KHz; when the frequency offset error is greater than or equal to 3KHz, the signal acquisition probability drops to close to zero.

[0054] The comparison results above show that the signal acquisition algorithm based on repetitive pilot autocorrelation proposed in this invention effectively solves the signal acquisition problem under large frequency offset error. It has better signal acquisition performance in low signal-to-noise ratio and large frequency offset scenarios, effectively improving signal quality and reliability, and improving the performance of communication systems.

[0055] This invention relates to a transmitter pilot design scheme based on repetitive pilots. The transmitter pilots employ a double-pulse insertion method, with each segment of the transmitted signal followed by a corresponding repetitive pilot. A signal acquisition algorithm based on the autocorrelation of the received signal pilot sequence is also included. The receiver does not need to store local pilots; instead, it utilizes a predefined sliding detection window to perform sliding autocorrelation processing on signals entering the window. The absolute value of the autocorrelation peak value within the sliding detection window is compared with a signal detection decision threshold. If the peak value is greater than the threshold, signal acquisition is complete.

Claims

1. A signal pilot design and acquisition method for scenarios with large frequency offset, characterized in that... Includes the following steps: Step 1: Let x be the baseband signal generated by the transmitter. When there is a carrier frequency offset f... e When the carrier phase shifts by φ, the received signal is... Where w is Gaussian white noise and t represents the sampling time; The transmitting pilot sequence satisfies the requirement that the signal sequence contains two identical pilot signals P. m ={p1,p2,...,p m Two identical pilot signals are inserted consecutively or intermittently to fill in other data; Step 2: At the receiving end, two signal sliding detection windows are set. The time length of a single sliding detection window is Δt, which is the same as the length of the pilot signal. The signal within the sliding detection window moves forward one sampling point at a time. Let the current received signal be r(t). Each time the sampled signal within the sliding detection window moves forward, the signal r within sliding detection window 1 is... w1 (t) and the signal r within the sliding detection window 2 w2 Perform conjugate correlation operations on (t) to obtain the conjugate multiplication sequence c(t); After obtaining c(t), the conjugate multiplication sequences within the sliding detection window are accumulated, and the absolute value of the superposition result is obtained: Among them, c r The acquisition correlation peak value is obtained by the receiver using the pilot sequence, and t0 is the start time of the sliding correlation calculation; The specific steps of conjugate correlation operation are as follows: First, calculate the conjugate multiplication sequence c(t) of the signals within the window: in, Indicates r w2 The conjugate of (t); when both repeated pilot sequences are completely within the sliding detection window, pilot signal 1 enters sliding detection window 1, and pilot signal 2 enters sliding detection window 2, i.e., r w1 (t)=r p1 And r w2 (t)=r p2 The conjugate multiplication of the two sliding detection window signals is calculated as follows: in, This represents the Gaussian white noise term, which is ignored in the calculation because Δt is a fixed value. The frequency offset is considered as a fixed phase offset caused by the accumulation of frequency offset over time. After conjugate multiplication, the frequency offset error term in the original received signal is eliminated. The magnitude of the subsequent signal acquisition related peak is no longer affected by the frequency offset, but is only related to the signal-to-noise ratio of the signal. Step 3: Obtain the capture correlation peak c of the pilot sequence r Then, c r With the set capture threshold T cap Compare; Step 4: If c r >T cap If c, it means a valid signal has been captured; if c r ≤T cap If no valid signal is found, return to step 2 to continue the signal search and capture process.

2. The signal pilot design and acquisition method for scenarios with large frequency offset as described in claim 1, characterized in that: In step 1, taking continuous pilot insertion as an example, the transmitted signal after pilot insertion is tx = {{P} m },{P m },{D n }}, where D n ={d1,d2,...,d n The data sequence is a baseband user data sequence. The two pilot signals received by the receiver are as follows: Where, r p1 and r p2 These are the received pilot signal 1 and pilot signal 2, respectively; Δt is the time interval between the two pilot signals, which is also the time length of a single pilot sequence in the repeated pilot continuous insertion mode; w1 and w2 are the Gaussian white noise contained in the two pilot signals, respectively.

3. An electronic device, characterized in that, include: One or more processors; Memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs being configured to perform the method as described in any one of claims 1-2.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method as described in any one of claims 1-2.

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